In this workshop, you’ll learn how to train, accelerate, and optimize a defect detection classifier. We’ll start by exploring the key challenges around industrial inspection, problem formulation, and data curation, exploration, and formatting. Then, you’ll learn about the fundamentals of transfer learning, online augmentation, modeling, and fine-tuning. By the end of the workshop, you’ll be familiar with the key concepts of optimized inference, performance assessment, and interpretation of deep learning models.
Learning Objectives
By participating in this workshop, you’ll learn how to:
- Formulate an industrial inspection case study and curate datasets generated by automated optical inspection (AOI) machines
- Deal with the logistics and challenges of data handling in an industrial inspection workflow
- Extract meaningful insights from our dataset using pandas DataFrame and NumPy library
- Apply transfer learning to a deep learning classification model (Inception v3)
- Fine-tune the deep learning model and set up evaluation metrics
- Optimize the trained Inception v3 model on an NVIDIA V100 Tensor Core GPU using NVIDIA® TensorRT™ 5
- Experiment with FP16 half-precision fast inferencing with the V100’s Tensor Cores
Download workshop datasheet (PDF 83.3 KB)
